Open isaacwasserman opened 2 years ago
I tried to reproduce your results, but after loading the pretrained weights, I can only get the model to predict [1,0]. Am I doing this correctly?
[1,0]
import tensorflow as tf import tensorflow.keras as keras import numpy as np model = keras.models.load_model("wb_model.h5") val_x_file = open("../datasets/DiabeticRetinopathyValData/val_test_x_preprocess.npy", "rb") val_y_file = open("../datasets/DiabeticRetinopathyValData/val_test_y.npy", "rb") val_x = np.load(val_x_file) val_y = np.load(val_y_file) p = np.random.permutation(len(val_y)) val_x = val_x[p] val_y = val_y[p] y_pred = model.predict(val_x) print("Prediction distribution:", np.histogram(np.argmax(y_pred, axis=1), range=(0,1), bins=2,)[0]) print("Real distribution:", np.histogram(np.argmax(val_y, axis=1), range=(0,1), bins=2,)[0])
Returns:
Prediction distribution: [10644 0] Real distribution: [8715 1929]
The CXR model has a similar issue, predicting 7/699 positives. The DERM model does significantly better, predicting 95/135 positives.
I tried to reproduce your results, but after loading the pretrained weights, I can only get the model to predict
[1,0]
. Am I doing this correctly?Returns: